First analysis and graphs test from the dataset
Sex Distribution
Code:
pie3D(c(women,men),main = "Sex Distribution",
labels = c(paste(women,"%"),paste(men,"%")),
col = c("turquoise","lightblue"),labelcex = 1,
explode = 0.1,start = 3 ,theta = 1.2, mar = c(6,6,6,6))
legend(-0.9,-0.8,c("Women","Men"),fill = c("turquoise","lightblue")) In this graph we can observe the sex distribution. There is a much higher percentage of men in the dataset.
Diabetic Pacients Distribution
Code:
pie3D(c(diabPerc,notDiabPerc),main = "Diabetics Distribution",
labels = c(paste(diabPerc,"%"),paste(notDiabPerc,"%")),
col = c("red","lightgreen"),labelcex = 1, explode = 0.1,start = 2.9 ,
theta = 1.1, mar = c(6,6,6,6))
legend(-1.1,1.15,c("Diabetic","Non Diabetic"),fill = c("red","lightgreen")) Almost one out of three patients is diabetic.
Women older than 50 y/o Density Distribution
Code:
womenMoreThan50 <- dataset[which(dataset$EDAD > 50 & dataset$SEXO == "FEME"), ]
womenMoreThan50 %>%
ggplot(aes(x=EDAD)) +
geom_density(fill = "#b19cd9", color = "#e1ecef", alpha = 0.8) +
ggtitle("Women older than 50 y/o Density") In this graph it is possible to visually understand how women are distributed according to their age.
Males and women older than 50 y/o Density Distribution
Code:
ggplot(dataset, aes(x = EDAD, fill = SEXO)) +
geom_density(alpha = 0.4) +
theme_ridges() +
scale_fill_discrete(name = "Gender", labels = c("Female","Male")) +
theme(legend.position = "top") In this graph it is possible to visually understand how women and men are distributed according to their age.
Procedure on diabetics patients
Code:
diab <- dataset[which(dataset$DIABETES == "1"),] #agregarle transparencia al color de las areas
diab %>%
ggplot(aes(x= EDAD, fill = PROCEDIMIENTO)) +
geom_density(alpha = 0.8) +
theme_ridges() +
scale_fill_discrete(name = "Procedimiento", labels = c("Cirugia","Angioplastia","Endovalvula")) +
theme(legend.position = "top") ggtitle("Densidad de diferentes procedimientos en pacientes diabeticos segun la edad")## $title
## [1] "Densidad de diferentes procedimientos en pacientes diabeticos segun la edad"
##
## attr(,"class")
## [1] "labels"
Procedure on diabetic patients
Code:
diabFiltered <- diab[which(diab$TIPO.DE.CIRUGIA != "#N/A"),]
diabFiltered %>%
ggplot(aes(x=TIPO.DE.CIRUGIA)) +
geom_bar( ) +
scale_fill_brewer() theme(legend.position="top")## List of 1
## $ legend.position: chr "top"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE
ggtitle("Density of different procedures on diabetic^-1 patients")## $title
## [1] "Density of different procedures on diabetic^-1 patients"
##
## attr(,"class")
## [1] "labels"
Procedures on patients with ejection fraction less than 50
Code:
plot_ly(
y = c(freqCirugiaFEY,freqAngioFEY,freqEndovalvFEY),
x = xVar,
name = "PROCEDIMIENTO DE FEY < 50 ",
type = "bar",
marker = list(color = c("#83DC4D","#0C9B21","#7BBB84","#9DFBAB"))) %>%
layout(title = "Procedimientos de pacientes con fraccion de eyeccion menor a 50")Procedures on patients younger than 50 y/o
Code:
edadMenorA55 <- dataset[which(dataset$EDAD < 55),]
procedimientosAge55 <- factor(edadMenorA55$PROCEDIMIENTO)
freqTable2 <- as.vector(table(procedimientosAge55))
freqAngio55 <- as.numeric(freqTable2[1])
freqCirugia55 <- as.numeric(freqTable2[2])
freqEndovalv55 <- 0
plot_ly(
y = c(freqCirugia55,freqAngio55,freqEndovalv55),
x = xVar,
name = "Procedimientos de pacientes con edad menor a 55",
type = "bar",
marker = list(color = c("E9ADF9","F9ADBD","F9ADE3","F9C3AD"))) %>%
layout(title = "Procedimientos de pacientes con edad menor a 55")Prodecures on patients older than 70 y/o
Code:
edadMayorA70 <- dataset[which(dataset$EDAD > 70),]
procedimientos <- factor(edadMayorA70$PROCEDIMIENTO)
freqTable3 <- as.vector(table(procedimientos))
freqAngio <- as.numeric(freqTable3[1])
freqCirugia <- as.numeric(freqTable3[2])
freqEndovalv <- as.numeric(freqTable3[4])
plot_ly(
y = c(freqCirugia,freqAngio,freqEndovalv),
x = xVar,
name = "Procedimientos de pacientes con edad mayor a 70",
type = "bar",
marker = list(color = c("E9ADF9","F9ADBD","F9ADE3","F9C3AD"))) %>%
layout(title = "Procedimientos de pacientes con edad mayor a 70") Procedures on patients
Code:
datasetFilteredPocedimiento <- dataset[which(dataset$PROCEDIMIENTO != "CIRUGIA, ENDOVALVULA"),]
datasetFilteredPocedimiento %>%
ggplot(aes(x= EDAD, fill = PROCEDIMIENTO)) +
geom_density(alpha = 0.8) +
theme_ridges() +
scale_fill_discrete(name = "Procedimiento", labels = c("Cirugia","Angioplastia","Endovalvula")) +
theme(legend.position = "top")+
ggtitle("Densidad de diferentes procedimientos segun la edad") ## Graphs {.tabset .tabset-fade .tabset-pills .unnumbered}
<50 y/o
!
>70 y/o
!